Abstract
Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.
Highlights
Cardamom is widely used as a flavoring agent and is widely used in medicine, including allopathy and Ayurveda [1]
Tassis et al proposed an approach by employing mask R-Convolutional Neural Network (CNN), for instance, segmentation and removal of the background using semantic segmentation by employing UNet and attained a 94.27% detection accuracy on coffee plant disease detection [45]
PROPOSED METHOD In this study, we proposed a cardamom plant leaf disease detection approach by employing a background removal technique to remove the complex background of the image by using U2-Net
Summary
Cardamom is widely used as a flavoring agent and is widely used in medicine, including allopathy and Ayurveda [1]. Pests, and weeds threaten production and quality farming, resulting in crop loss and economic loss. Agricultural, and plant pathology experts visit the farmland or farmers to identify plant disorders and pests based on acquaintances This approach is humble, and ambitious and ineffective. This has resulted in indispensable economic losses To address these challenges, image processing using an automatic plant leaf disease detection approach is essential. The detection of cardamom plant disease was proposed. Cardamom plant leaf images are captured in the farm field with complex backgrounds and a dataset generated, which measures the detection ability of the proposed approach. A cardamom plant disease detection approach was proposed using EfficientNetV2.
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